Since their invention and widespread availability, spreadsheets have provided business managers with a powerful tool to use during forecasting and planning.
Data warehouses and other repositories of large amounts of data in the form of multi-dimensional data stores are more and more a feature of business and electronic commerce, especially in enterprise-wide situations. The potential for improving business processes is large, and many tools are becoming available to assist the managers in their quest for improving forecasts. Many such tools extract data from the database or data warehouse and permit the manager to manipulate that data to perform ‘what-if’ operations which in turn allow the manager to explore a large number of scenarios.
An information platform carries out the following major functions, among others:                Collects and integrates data, observations and intelligence in a data warehouse;        Provides controls for multiple methods of information navigation and analysis;        Allows details to be digested in the context of other data, regardless of its type.        
An information platform may be implemented as a client/server system having the following four major functions.
Data gathering: The entire information platform relies upon reliable, predictable access to data, regardless of data source. The data retrieval section of the platform provides a sophisticated source of internal business information. The information platform provides users with multiple ways for collecting vast amounts and varied types of information in the shortest time possible.
Data Storage: The second section of the application platform handles the storage of the information once it has been gathered from a source. The information store uses a database (which may be object-oriented or relational) and is effectively a data warehouse. The data in the repository or warehouse are normally static, but in some instances, dynamic or volatile data may be present. This planning database which we term a planning data repository (PDR) includes the ability to change and manipulate the data, compared to the relative stability of the data in a traditional data warehouse. Both exhibit the same flexible access behaviours, i.e. users can navigate the data and find information using a variety of navigation mechanisms as described in the next section.
Information Browsing, Query, Analysis, and Report Creation: A user can peruse and analyze information contained in the PDR. The user can also make changes to some of the forecast data, and a calculation engine ensures that the changes are consistent with other data. Reports can be generated on various data.
Desktop Integration: Typically the users access the PDR from client computers in the client/server environment. These client computers might include special client applications, or they might take advantage of the latest web browser technologies to provide ubiquitous and universal access.
Electronic spreadsheets are well-known in the art and implementations having the ability to deal with thousands or even millions of cells are not uncommon. Spreadsheet programs allow users to perform “what-if” scenarios. After a set of computational relationships has been entered into a worksheet, the spread of information can be recalculated using different sets of assumptions, with the results of each recalculation appearing almost instantaneously.
It is simple and understood in the art to create a conventional spreadsheet program written in a browser scripting language, of which JavaScript is a well-known example, which can be interpreted by many standard browsers, Netscape™ and Microsoft Internet Explorer™ being typical. Such scripting languages are used to build upon and extend the capabilities of Hyper-Text Markup Language (HTML).
Further, the use of spreadsheets to interact with data from a data warehouse in a limited way is well-known and understood in the art. In such implementations, the data selected by the user is downloaded from the data warehouse (in a quasi batch-mode operation) and ‘loaded’ into the spreadsheet where the various formulae and functions are applied, and the results displayed for the user.
It is also known for some spreadsheet and spreadsheet-like programs to communicate with external programs and data. Such programs can, for example, export “live” data from a worksheet to a document created in a word processing program. If the data in the worksheet changes, it changes in the document as well. Similarly, a worksheet may import “live” data obtained from an external database through inter-process communication. If the data in the database changes, the change will appear in the worksheet. These links between the spreadsheet and external programs are one-way: data is either sent to the external program or received from it, but not both. In all cases, the calculations are limited to those capable of completion using only the data copied and stored locally by the spreadsheet program, without continuing reference to an underlying data repository.
Other products based on spreadsheets with separate computation capabilities have been described (e.g. U.S. Pat. No. 5,893,123 “System and method of integrating a spreadsheet and external program having output data calculated automatically in response to input data from the spreadsheet”), where both the nature of the underlying data and computation capability are considerably different from this invention. In the case of the '123 patent, the underlying data and the computation capability are contained in an electronic circuit simulator.
There exist products which are very similar in appearance to typical spreadsheet programs, but again they lack full integration with any underlying centralized data store. One such product, BrainMatter1 by “AlphaBlox” is a spreadsheet application written entirely in JavaScript2 and Hyper-Text Markup Language (D-HTML).
1TM of AlphaBlox 
2Registered trademark of Sun Microsystems 
Gedafe (Generic Database Front-End) from Departement für Elektrotechnik, ETZ Zürich (http://www.isg.ee.ethz.ch/tools/gedafe/index.en.html) is a web-based database front-end that is database-application independent. The application code does not contain any information about what tables are present in the database or how the data is organized. This product relies on a full-featured SQL database server which permits definition not only of the format of the various tables and fields, but also of how tables are related to each other. These features allow the implementation of data integrity constraints inside the database itself so that the database server itself guarantees the integrity of the database, independently from the software used to access the database. Although such a front-end might read all the integrity constraints directly off the database and enforce them itself in order to provide faster response to the user, at the end of the day the database server will only accept data which follow the rules defined by the database programmer. Data integrity is enforced for all possible interactions with the database short of manipulation of the database structure and rules themselves, but it imposes a large load on the database server during insert and update operations. However, Gedafe is limited to maintaining database constraints that can be defined in SQL, such as foreign keys and min/max values. Further, specific application software is required at the client.
Despite the obvious benefits of programs such as BrainMatter and Gedafe, they have limitations in their ability to deal with large data warehouses or Planning Data Repositories, and do not take full advantage of the possibilities provided by the ability of multiple users accessing the data, more or less simultaneously.
What is needed is a means of overcoming the limitations of existing client applications. Such improvements would allow better use of the underlying data and other information stored in a Planning Data Repository or similar facility.